Let’s define some vectors which can be used for demonstrations:
manyNumbers <- sample( 1:1000, 20 )
manyNumbers
[1] 269 49 81 194 82 405 551 366 734 602 788 505 847 732 85 62 134 873 926 245
manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA
[1] 49 269 732 NA 82 551 734 788 194 245 62 366 405 NA 926 505 85 847 NA 81 873 134 602
duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers
[1] 2 5 4 5 4 1 2 3 4 2
letters
[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"
LETTERS
[1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y" "Z"
mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters
[1] "c" "o" "k" "e" "d" "U" "W" "K" "G" "I"
manyNumbersWithNA instead of manyNumbers.all( manyNumbers <= 1000 )
[1] TRUE
all( manyNumbers <= 500 )
[1] FALSE
any( manyNumbers > 1000 )
[1] FALSE
any( manyNumbers > 500 )
[1] TRUE
all( !is.na( manyNumbers ) )
[1] TRUE
any( is.na( manyNumbers ) )
[1] FALSE
Input: logical vector Output: vector of numbers (positions)
which( manyNumbers > 900 )
[1] 19
which( manyNumbersWithNA > 900 )
[1] 15
which( is.na( manyNumbersWithNA ) )
[1] 4 14 19
manyNumbers[ manyNumbers > 900 ] # indexing by logical vector
[1] 926
manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions
[1] 926
somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]
[1] 926
"A" %in% LETTERS
[1] TRUE
c( "X", "Y", "Z" ) %in% LETTERS
[1] TRUE TRUE TRUE
all( c( "X", "Y", "Z" ) %in% LETTERS )
[1] TRUE
all( mixedLetters %in% LETTERS )
[1] FALSE
any( mixedLetters %in% LETTERS )
[1] TRUE
mixedLetters[ mixedLetters %in% LETTERS ]
[1] "U" "W" "K" "G" "I"
mixedLetters[ !( mixedLetters %in% LETTERS ) ]
[1] "c" "o" "k" "e" "d"
manyNumbers %in% 300:600
[1] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
which( manyNumbers %in% 300:600 )
[1] 6 7 8 12
sum( manyNumbers %in% 300:600 )
[1] 4
NAsif_else( manyNumbersWithNA >= 500, "large", "small" )
[1] "small" "small" "large" NA "small" "large" "large" "large" "small" "small" "small" "small" "small" NA "large" "large" "small" "large" NA "small" "large"
[22] "small" "large"
if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )
[1] "small" "small" "large" "UNKNOWN" "small" "large" "large" "large" "small" "small" "small" "small" "small" "UNKNOWN" "large" "large" "small"
[18] "large" "UNKNOWN" "small" "large" "small" "large"
# here integer 0L is needed instead of real 0.0
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L )
[1] 0 0 732 NA 0 551 734 788 0 0 0 0 0 NA 926 505 0 847 NA 0 873 0 602
unique( duplicatedNumbers )
[1] 2 5 4 1 3
unique( c( NA, duplicatedNumbers, NA ) )
[1] NA 2 5 4 1 3
duplicated( duplicatedNumbers )
[1] FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE TRUE TRUE
which.max( manyNumbersWithNA )
[1] 15
manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]
[1] 926
which.min( manyNumbersWithNA )
[1] 1
manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]
[1] 49
range( manyNumbersWithNA, na.rm = TRUE )
[1] 49 926
manyNumbersWithNA
[1] 49 269 732 NA 82 551 734 788 194 245 62 366 405 NA 926 505 85 847 NA 81 873 134 602
sort( manyNumbersWithNA )
[1] 49 62 81 82 85 134 194 245 269 366 405 505 551 602 732 734 788 847 873 926
sort( manyNumbersWithNA, na.last = TRUE )
[1] 49 62 81 82 85 134 194 245 269 366 405 505 551 602 732 734 788 847 873 926 NA NA NA
sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )
[1] 926 873 847 788 734 732 602 551 505 405 366 269 245 194 134 85 82 81 62 49 NA NA NA
manyNumbersWithNA[1:5]
[1] 49 269 732 NA 82
order( manyNumbersWithNA[1:5] )
[1] 1 5 2 3 4
rank( manyNumbersWithNA[1:5] )
[1] 1 3 4 5 2
sort( mixedLetters )
[1] "c" "d" "e" "G" "I" "k" "K" "o" "U" "W"
manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )
[1] 5.5 8.5 1.5 5.5 1.5 10.0 3.0 8.5 5.5 5.5
rank( manyDuplicates, ties.method = "min" )
[1] 4 8 1 4 1 10 3 8 4 4
rank( manyDuplicates, ties.method = "random" )
[1] 7 8 1 5 2 10 3 9 4 6
v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v
[1] -1.00000000 -0.50000000 0.00000000 0.50000000 1.00000000 0.75134619 -0.08287224 0.93421333 -0.09851515 1.25550389 -0.82291997 0.65503815 -0.18582365 -0.74296432
[15] 1.15346329
round( v, 0 )
[1] -1 0 0 0 1 1 0 1 0 1 -1 1 0 -1 1
round( v, 1 )
[1] -1.0 -0.5 0.0 0.5 1.0 0.8 -0.1 0.9 -0.1 1.3 -0.8 0.7 -0.2 -0.7 1.2
round( v, 2 )
[1] -1.00 -0.50 0.00 0.50 1.00 0.75 -0.08 0.93 -0.10 1.26 -0.82 0.66 -0.19 -0.74 1.15
floor( v )
[1] -1 -1 0 0 1 0 -1 0 -1 1 -1 0 -1 -1 1
ceiling( v )
[1] -1 0 0 1 1 1 0 1 0 2 0 1 0 0 2
heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights
Amy Eve Bob
166 170 177
names( heights )
[1] "Amy" "Eve" "Bob"
names( heights ) <- c( "AMY", "EVE", "BOB" )
heights
AMY EVE BOB
166 170 177
heights[[ "EVE" ]]
[1] 170
expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )
# A tibble: 8 × 2
x y
<int> <chr>
1 1 a
2 1 b
3 2 a
4 2 b
5 3 a
6 3 b
7 NA a
8 NA b
combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a" "a" "a" "a" "b" "b" "b" "c" "c" "d"
[2,] "b" "c" "d" "e" "c" "d" "e" "d" "e" "e"
combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a" "a" "a" "a" "a" "a" "b" "b" "b" "c"
[2,] "b" "b" "b" "c" "c" "d" "c" "c" "d" "d"
[3,] "c" "d" "e" "d" "e" "e" "d" "e" "e" "e"
Copyright © 2023 Biomedical Data Sciences (BDS) | LUMC